electricsheepafrica/africa-zimbabwe-health-facilities

Zimbabwe: Health facilities

Publisher: OCHA Regional Office for Southern and Eastern Africa (ROSEA) · Source: HDX · License: cc-by · Updated: 2025-11-13

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Abstract

List of Health facilities in Zimbabwe

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-11-13. Geographic scope: ZWE.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Public health
Unit of observation	Subnational administrative unit observations
Rows (total)	1,690
Columns	43 (37 numeric, 6 categorical, 0 datetime)
Train split	1,352 rows
Test split	338 rows
Geographic scope	ZWE
Publisher	OCHA Regional Office for Southern and Eastern Africa (ROSEA)
HDX last updated	2025-11-13

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Variables

Geographic — province (Manicaland, Mashonaland East, Midlands), district (Mutasa, Chipinge, Mutare), longitude (range 25.8258–33.0345), latitude (range -22.3173–-15.7006), yearbuilt (range 0.0–2011.0) and 2 others.

Temporal — updated (range 1998.0–2013.0).

Identifier / Metadata — id1 (range 1.0–1686.0), id (range 0.0–1685.0), nameoffaci (ZRP, Chivi, Shamva), esa_source (HDX), esa_processed (2026-04-18).

Other — elevation (range 0.0–1569.0), ownership (range 0.0–9.0), numofdocto (range 0.0–3.0), numofnurse (range 0.0–41.0), numofnur_1 (range 0.0–21.0) and 25 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-zimbabwe-health-facilities")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
id1	float64	0.1%	1.0 – 1686.0 (mean 845.1445)
id	float64	2.0%	0.0 – 1685.0 (mean 662.1087)
province	object	0.0%	Manicaland, Mashonaland East, Midlands
district	object	0.0%	Mutasa, Chipinge, Mutare
longitude	float64	0.1%	25.8258 – 33.0345 (mean 30.5756)
latitude	float64	0.1%	-22.3173 – -15.7006 (mean -18.8716)
elevation	float64	0.9%	0.0 – 1569.0 (mean 87.397)
updated	float64	0.9%	1998.0 – 2013.0 (mean 2001.2042)
nameoffaci	object	0.1%	ZRP, Chivi, Shamva
ownership	float64	10.7%	0.0 – 9.0 (mean 5.0199)
yearbuilt	float64	4.7%	0.0 – 2011.0 (mean 297.0981)
typeoffaci	object	1.0%	Clinic, Rural Health Centre, Council Clinic
numofdocto	float64	4.0%	0.0 – 3.0 (mean 0.0265)
numofnurse	float64	4.0%	0.0 – 41.0 (mean 0.4418)
numofnur_1	float64	4.0%	0.0 – 21.0 (mean 0.244)
numofpcn	float64	4.0%	0.0 – 39.0 (mean 0.1041)
numofehts	float64	4.0%	0.0 – 4.0 (mean 0.0444)
numofpharm	float64	4.0%	0.0 – 2.0 (mean 0.0105)
numoflabte	float64	4.0%	0.0 – 5.0 (mean 0.008)
numofbeds	float64	4.0%	0.0 – 2500.0 (mean 2.6402)
numofmater	float64	4.0%	0.0 – 20.0 (mean 0.1763)
numofgener	float64	4.0%	0.0 – 140.0 (mean 0.6245)
cathmentpo	float64	4.0%	0.0 – 1616300.0 (mean 2154.8712)
distneares	float64	4.0%	0.0 – 565.0 (mean 5.4855)
hascommuni	float64	4.0%
hascommu_1	float64	4.0%
haswaterpi	float64	4.0%
haswaterun	float64	4.0%
haselectri	float64	4.0%
haselect_1	float64	4.0%
distnear_1	float64	4.0%
hassanitat	float64	4.0%
hassanit_1	float64	4.0%
hassanit_2	float64	4.0%
hassecurit	float64	4.0%
hassecur_1	float64	4.0%
hasroadtar	float64	4.0%
hasroadgra	float64	4.0%
hasinciner	float64	4.0%
hasautoway	float64	4.1%
hasdental	float64	4.0%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-18

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Numeric Summary

Column	Min	Max	Mean	Median
id1	1.0	1686.0	845.1445	845.0
id	0.0	1685.0	662.1087	652.5
longitude	25.8258	33.0345	30.5756	30.8722
latitude	-22.3173	-15.7006	-18.8716	-18.7445
elevation	0.0	1569.0	87.397	0.0
updated	1998.0	2013.0	2001.2042	1998.0
ownership	0.0	9.0	5.0199	5.0
yearbuilt	0.0	2011.0	297.0981	0.0
numofdocto	0.0	3.0	0.0265	0.0
numofnurse	0.0	41.0	0.4418	0.0
numofnur_1	0.0	21.0	0.244	0.0
numofpcn	0.0	39.0	0.1041	0.0
numofehts	0.0	4.0	0.0444	0.0
numofpharm	0.0	2.0	0.0105	0.0
numoflabte	0.0	5.0	0.008	0.0

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 2 column(s) with >80% missing values were removed: comments, type_edite. 4 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from OCHA Regional Office for Southern and Eastern Africa (ROSEA) and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_zimbabwe_health_facilities,
  title     = {Zimbabwe: Health facilities},
  author    = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/zimbabwe-health-facilities},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.